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This paper introduces an active learning approach to the fitting of machine learning interatomic potentials.
J. Sherman, W. J. Morrison, Adjustment of an inverse matrix corresponding to a change in one element of a given matrix, The Annals of Mathematical Statistics 21 (1) (1950) 124–127
1950
Earlier work this paper cites.
G. Kresse, J. Hafner, Ab initio molecular dynamics for liquid metals, Physical Review B 47 (1) (1993) 558
1993
Earlier work this paper cites.
doi:10.1103/PhysRevB.50.17953
P. E. Blöchl, Projector augmented-wave method, Physical Review B 50 (24) (1994) 17953 · 1994
Earlier work this paper cites.
G. Mills, H. Jónsson, G. K. Schenter, Reversible work transition state theory: application to dissociative adsorption of hydrogen, Surface Science 324 (2-3) (1995) 305–337
1995
Earlier work this paper cites.
G. Kresse, J. Furthmüller, Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set, Computational Materials Science 6 (1) (1996) 15–50
1996
Earlier work this paper cites.
G. Kresse, J. Furthmüller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set, Physical review B 54 (16) (1996) 11169
1996
Earlier work this paper cites.
doi:10.1103/PhysRevLett.77.3865
J. P. Perdew, K. Burke, M. Ernzerhof, Generalized gradient approximation made simple, Physical review letters 77 (18) (1996) 3865 · 1996
Earlier work this paper cites.
A. De Vita, R. Car, A novel scheme for accurate MD simulations of large systems, in: MRS Proceedings, Vol. 491, Cambridge Univ Press, 1997, p. 473
1997
Earlier work this paper cites.
A. F. Voter, Parallel replica method for dynamics of infrequent events, Physical Review B 57 (22) (1998) R13985
1998
Earlier work this paper cites.
A. F. Voter, M. R. Sørensen, Accelerating atomistic simulations of defect dynamics: hyperdynamics, parallel replica dynamics, and temperature-accelerated dynamics, in: MRS Proceedings, Vol. 538, Cambridge Univ Press, 1998, p. 427
1998
Earlier work this paper cites.
E. Artacho, D. Sánchez-Portal, P. Ordejón, A. García, J. M. Soler, Linear-scaling ab-initio calculations for large and complex systems, physica status solidi (b) 215 (1) (1999) 809–817
1999
Earlier work this paper cites.
M. Finnis, Interatomic forces in condensed matter, Vol. 1, OUP Oxford, 2003
2003
Earlier work this paper cites.
S. L. Frederiksen, K. W. Jacobsen, K. S. Brown, J. P. Sethna, Bayesian ensemble approach to error estimation of interatomic potentials, Physical review letters 93 (16) (2004) 165501
2004
Earlier work this paper cites.
G. Csányi, T. Albaret, M. Payne, A. De Vita, learn on the fly: a hybrid classical and quantum-mechanical molecular dynamics simulation, Physical review letters 93 (17) (2004) 175503
2004
Earlier work this paper cites.
C.-K. Skylaris, P. D. Haynes, A. A. Mostofi, M. C. Payne, Introducing ONETEP: Linear-scaling density functional simulations on parallel computers, The Journal of chemical physics 122 (8) (2005) 084119
2005
Earlier work this paper cites.
J. Behler, M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces, Physical review letters 98 (14) (2007) 146401
2007
Cited alongside, same era.
B. Settles, Active learning literature survey, Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)
2009
Cited alongside, same era.
D. R. Bowler, T. Miyazaki, Calculations for millions of atoms with density functional theory: linear scaling shows its potential, Journal of Physics: Condensed Matter 22 (7) (2010) 074207
2010
Cited alongside, same era.
doi:10.1103/PhysRevLett.104.136403
A. P. Bartók, M. C. Payne, R. Kondor, G. Csányi, Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons, Phys. Rev. Lett. 104 (2010) 136403 · 2010
Cited alongside, same era.
S. Goreinov, I. Oseledets, D. Savostyanov, E. Tyrtyshnikov, N. Zamarashkin, How to find a good submatrix, in: Matrix Methods: Theory, Algorithms, Applications, Word Scientific, 2010, pp. 247–256
M. Gastegger, P. Marquetand, High-dimensional neural network potentials for organic reactions and an improved training algorithm, Journal of chemical theory and computation 11 (5) (2015) 2187–2198
2015
Later among the works it cites.
S. Manzhos, R. Dawes, T. Carrington, Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces, International Journal of Quantum Chemistry 115 (16) (2015) 1012–1020
2015
Later among the works it cites.
S. K. Natarajan, T. Morawietz, J. Behler, Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials, Physical Chemistry Chemical Physics 17 (13) (2015) 8356–8371
2015
Later among the works it cites.
F. Faber, A. Lindmaa, O. A. von Lilienfeld, R. Armiento, Crystal structure representations for machine learning models of formation energies, International Journal of Quantum Chemistry 115 (16) (2015) 1094–1101
2015
Later among the works it cites.
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2010
Cited alongside, same era.
J. Behler, Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations, Physical Chemistry Chemical Physics 13 (40) (2011) 17930–17955
2011
Cited alongside, same era.
J. Behler, Atom-centered symmetry functions for constructing high-dimensional neural network potentials, The Journal of chemical physics 134 (7) (2011) 074106
2011
Cited alongside, same era.
M. Rupp, A. Tkatchenko, K.-R. Müller, O. A. Von Lilienfeld, Fast and accurate modeling of molecular atomization energies with machine learning, Physical review letters 108 (5) (2012) 058301
2012
Cited alongside, same era.
J. C. Snyder, M. Rupp, K. Hansen, K.-R. Müller, K. Burke, Finding density functionals with machine learning, Physical review letters 108 (25) (2012) 253002
2012
Cited alongside, same era.
A. P. Bartók, M. J. Gillan, F. R. Manby, G. Csányi, Machine-learning approach for one-and two-body corrections to density functional theory: Applications to molecular and condensed water, Physical Review B 88 (5) (2013) 054104
2013
Cited alongside, same era.
A. P. Bartók, R. Kondor, G. Csányi, On representing chemical environments, Physical Review B 87 (18) (2013) 184115
2013
Cited alongside, same era.
J. Behler, Representing potential energy surfaces by high-dimensional neural network potentials, Journal of Physics: Condensed Matter 26 (18) (2014) 183001
2014
Cited alongside, same era.
V. Botu, R. Ramprasad, Learning scheme to predict atomic forces and accelerate materials simulations, Physical Review B 92 (9) (2015) 094306
2015
Later among the works it cites.
doi:10.1103/PhysRevLett.114.096405
Z. Li, J. R. Kermode, A. De Vita, Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces, Phys. Rev. Lett. 114 (2015) 096405 · 2015
Later among the works it cites.
V. Botu, R. Ramprasad, Adaptive machine learning framework to accelerate ab initio molecular dynamics, International Journal of Quantum Chemistry 115 (16) (2015) 1074–1083
2015
Later among the works it cites.
J. R. Boes, M. C. Groenenboom, J. A. Keith, J. R. Kitchin, Neural network and ReaxFF comparison for Au properties, International Journal of Quantum Chemistry 116 (13) (2016) 979–987
2016
Closest in time.
P. E. Dolgirev, I. A. Kruglov, A. R. Oganov, Machine learning scheme for fast extraction of chemically interpretable interatomic potentials, AIP Advances 6 (8) (2016) 085318
2016
Closest in time.
A. V. Shapeev, Moment tensor potentials, Multiscale Model. Simul. 14 (3) (2016) 1153–1173
2016
Closest in time.
N. Artrith, A. Urban, An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for tio 2, Computational Materials Science 114 (2016) 135–150
2016
Closest in time.
doi:10.1039/C6SC05720A
J. S. Smith, O. Isayev, A. E. Roitberg, Ani-1: an extensible neural network potential with DFT accuracy at force field computational cost, Chem. Sci. 21 (1) (2017) 124–127 · 2017
Closest in time.
doi:10.1103/PhysRevB.95.094203
V. L. Deringer, G. Csányi, Machine learning based interatomic potential for amorphous carbon , Phys. Rev. B 95 (2017) 094203 · 2017
Closest in time.
doi:10.1103/PhysRevB.95.214302
A. Glielmo, P. Sollich, A. De Vita, Accurate interatomic force fields via machine learning with covariant kernels , Phys. Rev. B 95 (2017) 214302 · 2017
Closest in time.
V. Botu, J. Chapman, R. Ramprasad, A study of adatom ripening on an al (111) surface with machine learning force fields, Computational Materials Science 129 (2017) 332–335
2017
Closest in time.